Bosses dont often play down their products. Sam Altman, the CEO of artificial intelligence company OpenAI, did just that when people went gaga over his companys latest software: the Generative Pretrained Transformer 3 (GPT-3). For some, GPT-3 represented a moment in which one scientific era ends and another is born. Mr Altman rightly lowered expectations. The GPT-3 hype is way too much, he tweeted last month. Its impressive but it still has serious weaknesses and sometimes makes very silly mistakes.

OpenAIs software is spookily good at playing human, which explains the hoopla. Whether penning poetry, dabbling in philosophy or knocking out comedy scripts, the general agreement is that the GPT-3 is probably the best non-human writer ever. Given a sentence and asked to write another like it, the software can do the task flawlessly. But this is a souped up version of the auto-complete function that most email users are familiar with.

GPT-3 stands out because it has been trained on more information about 45TB worth than anything else. Because the software can remember each and every combination of words it has read, it can work out through lightning-fast trial-and-error attempts of its 175bn settings where thoughts are likely to go. Remarkably it can transfer its skills: trained as a language translator, GPT-3 worked out it could convert English to Javascript as easily as it does English to French. Its learning, but not as we know it.

But this is not intelligence or creativity. GPT-3 doesnt know what it is doing; it is unable to say how or why it has decided to complete sentences; it has no grasp of human experience; and cannot tell if it is making sense or nonsense. What GPT-3 represents is a triumph of one scientific paradigm over another. Once machines were taught to think like humans. They struggled to beat chess grandmasters. Then they began to be trained with data to, as one observer pointed out, discover like we can rather than contain what we have discovered. Grandmasters started getting beaten. These days they cannot win.

The reason is Moores law, the exponentially falling cost of number-crunching. AIs bitter lesson is that the more data that can be consumed, and the more models can be scaled up, the more a machine can emulate or surpass humans in quantitative terms. If scale truly is the solution to human-like intelligence then GPT-3 is still about 1,000 times smaller than the brains 100 trillion-plus synapses. Human beings can learn a new task by being shown how to do it only a few times. That ability to learn complex tasks from only a few examples, or no examples at all, has so far eluded machines. GPT-3 is no exception.

All this raises big questions that seldom get answered. Training GPT-3s neural nets is costly. A $1bn investment by Microsoft last year was doubtless needed to run and cool GPT-3s massive server farms. The bill for the carbon footprint a large neural net is equal to the lifetime emissions of five cars is due.

Fundamental is the regulation of a for-profit OpenAI. The company initially delayed the launch of its earlier GPT-2, with a mere 1.5bn parameters, because the company fretted over its implications. It had every reason to be concerned; such AI will emulate the racist and sexist biases of the data it swallows. In an era of deepfakes and fake news, GPT-style devices could become weapons of mass destruction: engaging and swamping political opponents with divisive disinformation. Worried? If you arent then remember that Dominic Cummings wore an OpenAI T-shirt on his first day in Downing Street.

Read this article:

The Guardian view on artificial intelligence's revolution: learning but not as we know it - The Guardian

[co-author: Jordan Rhodes]

As the world grapples with the impacts of the COVID-19 pandemic, we have become increasingly reliant on artificial intelligence (AI) technology. Experts have used AI to test potential treatments, diagnose individuals, and analyze other public health impacts. Even before the pandemic, businesses were increasingly turning to AI to improve efficiency and overall profit. Between 2015 and 2019, the adoption of AI technology by businesses grew more than 270 percent.

The growing reliance on AIand other machine learning systemsis to be expected considering the technologys ability to help streamline business processes and tackle difficult computational problems. But as weve discussed previously, the technology is hardly the neutral and infallible resource that so many view it to be, often sharing the same biases and flaws as the humans who create it.

Recent research continues to point out these potential flaws. One particularly important flaw is algorithm bias, which is the discriminatory treatment of individuals by a machine learning system. This treatment can come in various forms but often leads to the discrimination of one group of people based on specific categorical distinctions. The reason for this bias is simpler than you may think. Computer scientists have to teach an AI system how to respond to data. To do this, the technology is trained on datasetsdatasets that are both created and influenced by humans. As such, it is necessary to understand and account for potential sources of bias, both explicit and inherent, in the collection and creation of a dataset. Failure to do so can result in bias seeping into a dataset and ultimately into the results and determinations made by an AI system or product that utilizes that dataset. In other words, bias in, bias out.

Examining AI-driven hiring systems expose this flaw in action. An AI system can sift through hundreds, if not thousands, of rsums in short periods of time, evaluate candidates answers to written questions, and even conduct video interviews. However, when these AI hiring systems are trained on biased datasets, the output reflects that exact bias. For example, imagine a rsum-screening machine learning tool that is trained on a companys historical employee data (such as rsums collected from a companys previously hired candidates). This tool will inherit both the conscious and unconscious preferences of the hiring managers who previously made all of those selections. In other words, if a company historically hired predominantly white men to fill key leadership positions, the AI system will reflect that preferential bias for selecting white men for other similar leadership positions. As a result, such a system discriminates against women and people of color who may otherwise be qualified for these roles. Furthermore, it can embed a tendency to discriminate within the companys systems in a manner that makes it more difficult to identify and address. And as the countrys unemployment rate skyrockets in response to the pandemic, some have taken issue with companies relying on AI to make pivotal employment decisionslike reviewing employee surveys and evaluations to determine who to fire.

Congress has expressed specific concerns regarding the increase in AI dependency during the pandemic. In May, some members of Congress addressed a letter to House and Senate Leadership, urging that the next stimulus package include protections against federal funding of biased AI technology. If the letters recommendations are adopted, certain businesses that receive federal funding from the upcoming stimulus package will have to provide a statement certifying that bias tests were performed on any algorithms the business uses to automate or partially automate activities. Specifically, this testing requirement would apply to companies using AI to make employment and lending determinations. Although the proposals future is uncertain, companies invested in promoting equality do not have to wait for Congress to act.

In recent months, many companies have publicly announced initiatives to address how they can strive to reduce racial inequalities and disparities. For companies considering such initiatives, one potential actionable step could be a strategic review of the AI technology that a company utilizes. Such a review could include verifying whether the AI technology utilized by the company is bias-tested and consideration of the AI technologys overall potential for automated discriminatory effects given the context of its specific use.

Only time will reveal the large-scale impacts of AI on our society and whether weve used AI in a responsible manner. However, in many ways, the pandemic demonstrates that these concerns are only just beginning.

[View source.]

Read the original:

Digitalized Discrimination: COVID-19 and the Impact of Bias in Artificial Intelligence - JD Supra

UK-based Metro Bank has announced details of its collaboration with Canadian tech firm Sensibill to provide business customers with enhanced AI tools.

Specifically, new features like receipt management capabilities will be added to Metro Banks app, providing SMBs with a simple but powerful method of capturing and storing records of their transactions.

For users the process is simple: photographs of receipts are taken with a devices in-built camera and then AI (artificial intelligence) and ML (machine learning) software are used to auto-populate the users transaction history, including VAT.

With UK SMBs projected to lose up to 15 cumulative days per year while trying to balance company expenditure records (two hours each week), the utility of an easy, automated solution for businesses is clear.

Were thrilled to partner with Sensibill to provide our business customers with essential money management tools, easily accessible from our mobile app. These will empower SMBs to free up time in a way that wasnt possible before, to spend running and growing their businesses, said David Thomasson, Chief Commercial Officer at Metro Bank.

So many small businesses are facing uncertainty because of coronavirus. We want to keep delivering new tools for our customers that can make managing their money a little easier.

Helping customers build efficiency

The rollout of Sensibill comes following a successful trial period in 2019. Sensibill is dedicated to improving customer engagement and creating mutual understanding between them and the financial services institutions that serve them.

Winner of the Best Mobile Banking Innovation award at last years Financial Innovation Awards, the companys collaboration with Metro Bank signifies its potential for expanding even further across the UK finance market.

Regarding the announcement that Metro Bank considered the trial period successful, Corey Gross, co-founder and CEO, commented, [It] understands that small businesses and gig workers need a better, simpler way to track their finances and manage expenses.

By leveraging our solution, the banks small businesses can regain hours once lost to analysing paper receipts and run their businesses more effectively, which is especially critical in light of the pandemic.

This partnership reflects Metro Banks deep dedication to providing advanced technology and support to help the people they serve succeed financially, both now and in the future.

The upgraded app is currently only available on iOS. However, Metro Bank assures Android users that its new features will be made available to them in the coming weeks.

The rest is here:

Metro Bank and Sensibill partner on AI money management | Technology & AI - FinTech Magazine - The FinTech & InsurTech Platform

Best listening experience is on Chrome, Firefox or Safari. Subscribe to Federal Drives daily audio interviews onApple PodcastsorPodcastOne.

Artificial intelligence is steadily making its way into federal agency operations. Its a type of software that can speed up decision-making, and grow more useful with more data. A problem is that if youre not careful, the algorithms in AI software can introduce unwanted biases. And therefore produce skewed results. Its a problem researchers at the National Institute of Standards and Technology have been working on. With more, the chief of staff of NISTs information technology laboratory, Elham Tabassi, joinedFederal Drive with Tom Temin.

Tom Temin: Mr. Tabassi, good to have you on.

Elham Tabassi: Thanks for having me.

Tom Temin: Lets begin at the beginning here. And we hear a lot about bias in artificial intelligence. Define for us what it means.

Elham Tabassi: Thats actually a very good question and a question that researchers are working on this, and a question that we are trying to find an answer along with the community, and discuss this during the workshop thats coming up in August. Its often the case that we all use the same term meaning different things. We talk about it as if you know exactly what were talking about, and bias is one of those terms. The International Standards Organization, ISO, has a subcommittee working on standardization of bias, and they have a document that with collaborations of experts around the groups are trying to define bias. So one there isnt a good definition for bias yet. What we have been doing at NIST is doing a literature survey trying to figure out how it has been defined by different experts, and we will discuss it further at the workshop. Our goal is to come up with a shared understanding of what bias is. I avoid the term definition and talk about the shared understanding of what bias is. The current draft of standards and the current sort of understanding of the community is going towards that bias is on in terms of disparities in error rates and performance for different populations, different devices or different environments. So one point I want to make here is what we call bias may be designed in. So if you have different error rates for different subpopulations, face recognition that you mentioned, thats not a good bias and something that has to be mitigated. But sometimes, for example, for car insurance, it has been designed in a way that certain populations, younger people pay more insurance at a higher insurance rate than people in their 40s or 50s, and that is by design. So just the difference in error rate is not bias on intended behavior or performance of the system. Its something thats problematic and needs to be studied.

Tom Temin: Yeah, maybe a way to look at it is If a persons brain had all of the data that the AI algorithm has, and that person was an expert and would come up with a particular solution, and theres a variance between what that would be and what the AI comes up with that could be a bias.

Elham Tabassi: Yes, it could be but then lets not forget about human biases, and that is actually one source of bias in AI systems. The bias in AI system can creep in in different ways. They can creep into algorithm because AI systems learn to make decisions based on the training data, which can include biased human decisions or reflect historical or societal inequalities. Sometimes the bias creeps in because the data has been not the right representative of the whole population, the sampling was done that one group is over represented or underrepresented. Another source of bias can be in the design of the algorithm and in the modeling of that. So biases can creep in in different ways and sometimes the human biases exhibit itself into the algorithm, sometimes algorithm modeling and picked up some biases.

Tom Temin: But you could also get bias in AI systems that dont involve human judgment or judgment about humans whatsoever. Say it could be a AI program running a process control system or producing parts in a factory, and you could still have results that skew beyond what you want over time because of a bias built in thats of a technical nature. Would that be fair to say?

Elham Tabassi: Correct, yes. So if the training data set is biased or not representative of space of the whole possible input, then you have bias. One real research question is how to mitigate and unbias the data. Another one is that if during the algorithm biases if theres anything during the design and building in a model, that it can be bias, that can introduce bias, the way the models are developed.

Tom Temin: So nevertheless, agencies have a need to introduce these algorithms and these programs into their operations and theyre doing so. What are some of the best practices for avoiding bias in the outcomes of your AI system?

Elham Tabassi: The research is still out there. This is one of those cutting edge research and we see a lot of good research and results coming out from AI experts every day. But really to mitigate bias, to measure bias and mitigate bias, the first step really is to understand what biases and thats your first question. So unless we know what it is that we want to measure, and we have a consensus and understanding and agreement on what it is that we want to measure, which goes back to that shared understanding of bias or definition of bias, its hard to get into the measurement. So we are spending a little bit more time on getting everybody on the same page on understanding what bias is so we know what it is that we want to measure. Then we get into the next step of how to measure, which is the development of the metrics for understanding and examining and measuring bias in systems. And it can be measured biases in the data and the algorithm, so on so forth. Then its even after these two steps that we can talk about the best practices or the best way of mitigation of the bias. So we are still a bit early in understanding on how to measure because we dont have a good grip on what it is that we want to measure.

Tom Temin: But in the meantime, Ive heard of some agencies just simply using two or more algorithms to do the same calculation such that they be the biases in them can cancel one another out, or using multiple data sets that might have canceling biases in them just to make sure that at least theres balance in there.

Elham Tabassi: Right. Thats one way, and that goes back to what we talked at the beginning of the call about having a poor representation. And you just talked about having two databases, so that can mitigate the problem of the skewed representation or sampling. Just like that, in the literature there are many, many definitions of the bias already. Theres also many different methods and guidance and recommendations on what to do, but what we are trying to do is come up with a set of agreeable and unified way on how to do these things thing and that is still cutting edge research.

Tom Temin: Got it. And in the meantime, NIST is planning a workshop on bias in artificial intelligence. Tell us when and where and whats going to happen there.

Elham Tabassi: Right that workshop is going to be on August 18. Its a whole day workshop. Our plan was to have a demo today but because its virtual workshop, we decided to just have it as one day. The workshop is one of the workshop in a series that NIST plans to organize and have in coming months. The fields of the workshop that they are organizing and planning is trying to get at the heart of what constitutes trustworthiness, what are the technical requirements, what they are and how to measure them. Bias is one of those technical requirements and we have a dedicated workshop on bias on August 18 where we want them to be a interactive discussions with the participants and we have a panel in the morning. The whole morning is dedicated to discussions of the data and the bias in data, and how the biases in data can contribute to the bias into whole AI system. We have a panel in the morning, kind of as a stage setting panel that kind of frame the discussion for the morning and then it will be breakout sessions. Then in the afternoon, the same format and discussion will be around biases in the algorithm and how those can make an AI system biased.

Tom Temin: Who should attend?

Elham Tabassi: The AI developers, the people that are actually building the AI systems, the AI users, the people that want to use AI system. Policy makers will have a better understanding of the issues in AI system and bias in AI systems. People that want to use it, either the developer or the user of technology, and policymakers.

Tom Temin: If youre a program manager, or policymaker and your team is cooking up something with AI, you probably want to know what it is theyre cooking up in some detail, because youre gonna have to answer for it eventually I suppose.

Elham Tabassi: Thats right. And if I didnt emphasize it enough, of course at the research community because they are the one that we go to for innovation and solutions to the problem/

Tom Temin: Elham Tabassi is chief of staff of the information technology laboratory at the National Institute of Standards and Technology. Thanks so much for joining me.

Elham Tabassi: Thanks for having me.

Read this article:

Tackling the problem of bias in AI software - Federal News Network

Imagine buying a dress online because a piece of code sold you on its flattering, feminine flair or convinced you romantic floral details would outline your figure with timeless style. The very same day your friend buy the same dress from the same website but shes sold on a description of vibrant tones, fresh cotton feel and statement sleeves.

This is not a detail from a sci-fi short story but the reality and big picture vision of Hypotenuse AI, a YC-backed startup thats using computer vision and machine learning to automate product descriptions for e-commerce.

One of the two product descriptions shown below is written by a human copywriter. The other flowed from the virtual pen of the startups AI, per an example on its website.

Can you guess which is which?* And if you think you can well, does it matter?

Screengrab: Hypotenuse AIs website

Discussing his startup on the phone from Singapore, Hypotenuse AIs founder Joshua Wong tells us he came up with the idea to use AI to automate copywriting after helping a friend set up a website selling vegan soap.

It took forever to write effective copy. We were extremely frustrated with the process when all we wanted to do was to sell products, he explains. But we knew how much description and copy affect conversions and SEO so we couldnt abandon it.

Wong had been working for Amazon, as an applied machine learning scientist for its Alexa AI assistant. So he had the technical smarts to tackle the problem himself. I decided to use my background in machine learning to kind of automate this process. And I wanted to make sure I could help other e-commerce stores do the same as well, he says, going on to leave his job at Amazon in June to go full time on Hypotenuse.

The core tech here computer vision and natural language generation is extremely cutting edge, per Wong.

What the technology looks like in the back end is that a lot of it is proprietary, he says. We use computer vision to understand product images really well. And we use this together with any metadata that the product already has to generate a very human fluent type of description. We can do this really quickly we can generate thousands of them within seconds.

A lot of the work went into making sure we had machine learning models or neural network models that could speak very fluently in a very human-like manner. For that we have models that have kind of learnt how to understand and to write English really, really well. Theyve been trained on the Internet and all over the web so they understand language very well. Then we combine that together with our vision models so that we can generate very fluent description, he adds.

Image credit: Hypotenuse

Wong says the startup is building its own proprietary data-set to further help with training language models with the aim of being able to generate something thats very specific to the image but also specific to the companys brand and writing style so the output can be hyper tailored to the customers needs.

We also have defaults of style if they want text to be more narrative, or poetic, or luxurious but the more interesting one is when companies want it to be tailored to their own type of branding of writing and style, he adds. They usually provide us with some examples of descriptions that they already have and we used that and get our models to learn that type of language so it can write in that manner.

What Hypotenuses AI is able to do generate thousands of specifically detailed, appropriately styled product descriptions within seconds has only been possible in very recent years, per Wong. Though he wont be drawn into laying out more architectural details, beyond saying the tech is completely neural network-based, natural language generation model.

The product descriptions that we are doing now the techniques, the data and the way that were doing it these techniques were not around just like over a year ago, he claims. A lot of the companies that tried to do this over a year ago always used pre-written templates. Because, back then, when we tried to use neural network models or purely machine learning models they can go off course very quickly or theyre not very good at producing language which is almost indistinguishable from human.

Whereas now we see that people cannot even tell which was written by AI and which by human. And that wouldnt have been the case a year ago.

(See the above example again. Is A or B the robotic pen? The Answer is at the foot of this post)

Asked about competitors, Wong again draws a distinction between Hypotenuses pure machine learning approach and others who relied on using templates to tackle this problem of copywriting or product descriptions.

Theyve always used some form of templates or just joining together synonyms. And the problem is its still very tedious to write templates. It makes the descriptions sound very unnatural or repetitive. And instead of helping conversions that actually hurts conversions and SEO, he argues. Whereas for us we use a completely machine learning based model which has learnt how to understand language and produce text very fluently, to a human level.

There are now some pretty high profile applications of AI that enable you to generate similar text to your input data but Wong contends theyre just not specific enough for a copywriting business purpose to represent a competitive threat to what hes building with Hypotenuse.

A lot of these are still very generalized, he argues. Theyre really great at doing a lot of things okay but for copywriting its actually quite a nuanced space in that people want very specific things it has to be specific to the brand, it has to be specific to the style of writing. Otherwise it doesnt make sense. It hurts conversions. It hurts SEO. So we dont worry much about competitors. We spent a lot of time and research into getting these nuances and details right so were able to produce things that are exactly what customers want.

So what types of products doesnt Hypotenuses AI work well for? Wong says its a bit less relevant for certain product categories such as electronics. This is because the marketing focus there is on specs, rather than trying to evoke a mood or feeling to seal a sale. Beyond that he argues the tool has broad relevance for e-commerce. What were targeting it more at is things like furniture, things like fashion, apparel, things where you want to create a feeling in a user so they are convinced of why this product can help them, he adds.

The startups SaaS offering as it is now targeted at automating product description for e-commerce sites and for copywriting shops is actually a reconfiguration itself.

The initial idea was to build a digital personal shopper to personalize the e-commerce experence. But the team realized they were getting ahead of themselves. We only started focusing on this two weeks ago but weve already started working with a number of e-commerce companies as well as piloting with a few copywriting companies, says Wong, discussing this initial pivot.

Building a digital personal shopper is still on the roadmap but he says they realized that a subset of creating all the necessary AI/CV components for the more complex digital shopper proposition was solving the copywriting issue. Hence dialing back to focus in on that.

We realized that this alone was really such a huge pain-point that we really just wanted to focus on it and make sure we solve it really well for our customers, he adds.

For early adopter customers the process right now involves a little light onboarding typically a call to chat through their workflow is like and writing style so Hypotenuse can prep its models. Wong says the training process then takes a few days. After which they plug in to it as software as a service.

Customers upload product images to Hypotenuses platform or send metadata of existing products getting corresponding descriptions back for download. The plan is to offer a more polished pipeline process for this in the future such as by integrating with e-commerce platforms like Shopify .

Given the chaotic sprawl of Amazons marketplace, where product descriptions can vary wildly from extensively detailed screeds to the hyper sparse and/or cryptic, there could be a sizeable opportunity to sell automated product descriptions back to Wongs former employer. And maybe even bag some strategic investment before then However Wong wont be drawn on whether or not Hypotenuse is fundraising right now.

On the possibility of bagging Amazon as a future customer hell only say potentially in the long run thats possible.

Joshua Wong (Photo credit: Hypotenuse AI)

The more immediate priorities for the startup are expanding the range of copywriting its AI can offer to include additional formats such as advertising copy and even some listicle style blog posts which can stand in as content marketing (unsophisticated stuff, along the lines of 10 things you can do at the beach, per Wong, or 10 great dresses for summer etc).

Even as we want to go into blog posts were still completely focused on the e-commerce space, he adds. We wont go out to news articles or anything like that. We think that that is still something that cannot be fully automated yet.

Looking further ahead he dangles the possibility of the AI enabling infinitely customizable marketing copy meaning a website could parse a visitors data footprint and generate dynamic product descriptions intended to appeal to that particular individual.

Crunch enough user data and maybe it could spot that a site visitor has a preference for vivid colors and like to wear large hats ergo, it could dial up relevant elements in product descriptions to better mesh with that persons tastes.

We want to make the whole process of starting an e-commerce website super simple. So its not just copywriting as well but all the difference aspects of it, Wong goes on. The key thing is we want to go towards personalization. Right now e-commerce customers are all seeing the same standard written content. One of the challenges there its hard because humans are writing it right now and you can only produce one type of copy and if you want to test it for other kinds of users you need to write another one.

Whereas for us if we can do this process really well, and we are automating it, we can produce thousands of different kinds of description and copy for a website and every customer could see something different.

Its a disruptive vision for e-commerce (call it A/B testing on steroids) that is likely to either delight or terrify depending on your view of current levels of platform personalization around content. That process can wrap users in particular bubbles of perspective and some argue such filtering has impacted culture and politics by having a corrosive impact on the communal experiences and consensus which underpins the social contract. But the stakes with e-commerce copy arent likely to be so high.

Still, once marketing text/copy no longer has a unit-specific production cost attached to it and assuming e-commerce sites have access to enough user data in order to program tailored product descriptions theres no real limit to the ways in which robotically generated words could be reconfigured in the pursuit of a quick sale.

Even within a brand there is actually a factor we can tweak which is how creative our model is, says Wong, when asked if theres any risk of the robots copy ending up feeling formulaic. Some of our brands have like 50 polo shirts and all of them are almost exactly the same, other than maybe slight differences in the color. We are able to produce very unique and very different types of descriptions for each of them when we cue up the creativity of our model.

In a way its sometimes even better than a human because humans tends to fall into very, very similar ways of writing. Whereas this because its learnt so much language over the web it has a much wider range of tones and types of language that it can run through, he adds.

What about copywriting and ad creative jobs? Isnt Hypotenuse taking an axe to the very copywriting agencies his startup is hoping to woo as customers? Not so, argues Wong. At the end of the day there are still editors. The AI helps them get to 95% of the way there. It helps them spark creativity when you produce the description but that last step of making sure it is something that exactly the customer wants thats usually still a final editor check, he says, advocating for the human in the AI loop. It only helps to make things much faster for them. But we still make sure theres that last step of a human checking before they send it off.

Seeing the way NLP [natural language processing] research has changed over the past few years it feels like were really at an inception point, Wong adds. One year ago a lot of the things that we are doing now was not even possible. And some of the things that we see are becoming possible today we didnt expect it for one or two years time. So I think it could be, within the next few years, where we have models that are not just able to write language very well but you can almost speak to it and give it some information and it can generate these things on the go.

*Per Wong, Hypotenuses robot is responsible for generating description A. Full marks if you could spot the AIs tonal pitfalls

View post:

Hypotenuse AI wants to take the strain out of copywriting for e-commerce - TechCrunch

The increased move to digitization is only one of several trends the healthcare industry has embraced in the past few years. Transferring paper-based information to digital formats gives health professionals faster access to data, but the benefits dont stop there. To turn the stored information into something useful, the industry needs systems that find patterns, recognize what is important and perform predictive analysis. On that basis, investors should consider AI stocks.

The digitization of healthcare-related data will involve companies that lead in Artificial Intelligence. The rise of AI will not lead to job losses for healthcare professionals, but instead enable companies to automate repetitive tasks and free their staff to do other, more valuable things.

Here are seven AI stocks to buy for the increasing digitization of healthcare:

How might AI-powered systems contribute to a better healthcare system? Electronic Healthcare Records (EHRs) have a rich dataset to back up the benefits of AI. As medical costs for patients increase at an uncontrollable rate, the industry will want to invest in AI solutions to lessen the load.

Source: Laborant / Shutterstock.com

International Business Machines reported lower year-over-year revenue for the second quarter. Revenue fell 5.42% Y/Y to $18.12 billion, though it earned $2.18 a share. Watson is a central brand for the AI solution IBM offers, as well as a part of its hybrid cloud strategy, which IBM advertises may help its clients work through both complex and regulated workloads.

According to IBM, Watson helps you predict and shape future outcomes, automate complex processes, and optimize your employees time. For example, AI will help healthcare professionals with surface treatment, supporting user needs, and by targeting similarities and patterns.

Data courtesy of Stockrover

As a tech stock, IBM trades at a steep price-to-earnings multiples well-below both industry and S&P 500 averages. Markets are punishing IBM stock for the slow growth in its legacy businesses.

IBM still has plenty of work ahead in building Watsons AI doctor. Until it gets beyond the hype and delivers on helping such things as making diagnoses, IBM will rely on business growth from its other business units. That includes Red Hat and Cloud Paks.

Source: StreetVJ / Shutterstock.com

China-based Baidu established a health internet hospital on March 18. It also recently established Baidu Health Technology. The company is committing to the online healthcare industry with strong experience in big data and AI technologies.

Baidus value score is on par with the index, as shown in the table below. As its role in healthcare increases, price-to-sales ratio will expand to match that of the industry average. Baidu stock will increase as a result:

Data courtesy of Stockrover

Baidu said last year that it would donate AI-integrated fundus screening machines to 500 medical centers. Already, the donation is paying off. The AI-powered camera detects eye fundus and creates a screening report in mere seconds. Because China has a shortage of ophthalmologists, Baidu is helping to increase the availability of patient care.

In the near term, the company will build its Baidu Health unit. This included holding more than 100 live broadcasting events on COVID-19. Baidu Health also helps users register for doctor appointments, get information on hospitals and doctors and connect with doctors for online consultation.

On Wall Street, the average price target for Baidu stock is $146.67 (per Tipranks).

Source: JHVEPhoto / Shutterstock.com

In 2019, Medtronic launched its first AI system for colonoscopy. The company said, The GIGenius module uses advanced artificial intelligence to highlight the presence of pre-cancerous lesions with a visual marker in real-time serving as an ever vigilant second observer. A new era of diagnostic endoscopy should improve the detection rate that a doctor may miss, ultimately saving more lives.

Data courtesy of Stockrover

Above, Medtronic stock scores a 92/100 on quality. The market is ignoring its strong gross margins relative to the S&P 500.

Chairman and CEO Omar Ishrak recently explained how the model for personalized medicine is becoming a reality. That will depend on developing AI solutions in the healthcare market. In doing so, the company will empower physicians.

By giving doctors clinical and behavioral data, providers will have more information available. Making better-informed decisions will increase the effectiveness of patient treatments.

Source: Shutterstock

More of Strykers customers are ordering robots. As robotic surgery procedures increase, the role of artificial intelligence in healthcare will rise in importance, too. Stryker is a leader in orthopedic robotics. In the second quarter, the company posted strong orders, thanks to its continued push for innovation. Joint replacement surgeries, for example, are growing above the market rate.

Data courtesy of Stockrover

Strykers price to free cash flow ratio is below that of the industry. Given its strong role in AI in healthcare, the Stryker stock is trading at a discount.

On its conference call, Strykers VP of Investor Relations, Preston Wells said, whether theyre competitive accounts that are in or out, were really just going to all of those different accounts and trying to find areas to place Mako.

Wells further implied the addressable market will get larger as customers ask for more solutions from Mako. The robotic-arm uses a 3D CT-based planning software. Surgeons will know more about the patients anatomy, enabling them to offer a personalized joint replacement.

Source: Shutterstock

Nuance shares have risen steadily from sub-$15 lows to around $27. In its second quarter, the company posted organic revenue growth of 11% Y/Y. Enterprise revenue grew 19%, the highest in 10 years. Dragon Medical One is the flagship growth driver for Nuance; demand for that service grew 46% Y/Y.

Below, most analysts rate Nuance stock with a strong-buy recommendation:

Data courtesy of Stockrover

Nuance accelerated its AI innovation and continued the development of machine learning-based tools. This will improve the workflow and productivity in healthcare. Dragon Medical One contributed to the strong first half annual recurring revenue growth.

Nuance scaled its international markets by launching Dragon Medical One in five new European countries. The product is a speech recognition cloud solution that will improve the productivity of healthcare workers. It securely captures the patients narrative and reduces the workload of clinicians.

The rise in telemedicine during the global pandemic will drive Nuances AI business higher.

Source: rvlsoft / Shutterstock.com

Googles mandate for Deepmind is building products that support care teams and improve patient outcomes. Google has expertise in cloud storage, data security and app development. It will work to develop mobile medical assistants for clinicians.

Data courtesy of Stockrover

Alphabets growth will outpace the S&P 500 index over the next year. The 95/100 growth score suggests the stock will outperform markets, too, in the year.

In diagnostics, Deepmind will help healthcare workers detect eye disease from scans or assist in cancer radiotherapy treatment. More recently, Googles pending acquisition of Fitbit will accelerate the search giants development of wearables in healthcare. And since these devices track the wearers health metrics, it will have plenty of user data to work with.

That volume of data will necessitate machine learning and AI to decipher any meaningful patterns. Without AI, Google cannot perform any initial diagnoses that may potentially save a wearers life.

Google hasnt gotten the European Unions blessing on the deal, and a full-scale investigation will delay the Fitbit acquisition. But should it clear, the companys positioning in AI in healthcare will strengthen.

Source: Kevin Chen Photography / Shutterstock.com

Alibaba has all the requisite backend systems in place for AI in healthcare. Alibaba Cloud has AI-powered solutions that are solving real-world problems. And BABA is solving healthcare problems by analyzing clinical and hospital operations.

The company said that the system uses 700 core indicators that come from medical institutions and regional medical operations. By feeding real-world data to the AI, the system will have higher accuracy and reliability. Its AI platform may perform image and voice recognition. Medical institutions get diagnosis support from Alibabas AI.

The real-world importance of Alibabas new AI system will save lives. The system has a 96% accuracy in detecting coronavirus in mere seconds. By contrast, it takes humans around 15 minutes to make a diagnosis.

The fair value of Alibaba stock is $325.72. The value score is low but the growth score is 100/100:

Data courtesy of Stockrover

Alibaba trained the system to detect coronavirus by introducing images and data from 5,000 confirmed coronavirus cases.

Disclosure: As of this writing, the author did not hold a position in any of the aforementioned securities.

View original post here:

7 AI Stocks to Buy for the Increasing Digitization of Healthcare - InvestorPlace

The sudden appearance and rapid spread of COVID-19 took governments and society by surprise. As they dusted off pandemic response plans and geared up to fight the virus, it became clear that we needed to turbo-charge R&D efforts and find better ways to hunt down promising treatments for emerging diseases.

Artificial intelligence (AI) has proven a powerful tool in this fight.

In a pandemic, speed is of the essence. Although scientists managed to sequence the genetic code of the new coronavirus and produce diagnostic tests in record time, developing drugs and vaccines against the virus remains a long haul.

AI has the power to accelerate the process by reasoning across all available biomedical data and information in a systematic search for existing approved medicines a vital step in helping patients while the world waits for a vaccine.

Machines excel in handling data in fast-changing circumstances, which means machine learning systems can be harnessed to work as tireless and unbiased super-researchers.

This is not just theory. In late January, using its proprietary platform of AI models and algorithms to search through the scientific literature, researchers at BenevolentAI in London identified an established, once-daily arthritis pill as a potential treatment for COVID-19. The findings were published in two papers in The Lancet and The Lancet Infectious Diseases, in line with our commitment under the Wellcome Trust pledge to share our coronavirus-related research rapidly and openly.

BenevolentAI's COVID-19 timeline

Image: BenevolentAI

The discovery followed a computer-driven hunt for drug candidates with both antiviral and anti-inflammatory properties, since in severe cases of COVID-19 it is the bodys overactive immune response that can cause significant and sometimes fatal damage.

The drug, baricitinib, is currently marketed by Eli Lilly to treat rheumatoid arthritis. Now, thanks to AI, it is being tested against COVID-19 in a major randomised-controlled trial in collaboration with the U.S. National Institute for Allergies and Infectious Diseases (NIAID) in combination with remdesivir, an antiviral drug from Gilead Sciences that recently won emergency-use approval for COVID-19. Eli Lilly has now commenced its own independent trial of baricitinib as a therapy for COVID-19 in South America, Europe and Asia.

The BenevolentAI knowledge graph found that baricitinib might help treat COVID-19.

Image: BenevolentAI

The system used to identify baricitinib was not actually set up to find new uses of existing medicines, but rather to discover and develop new drugs a sign of the potential for AI to uncover novel insights and relationships across an unlimited number of biological entities. In a crisis like COVID-19, it clearly makes sense to hunt through already approved drugs that can be ready for large-scale clinical trials until vaccines are approved and readily available in the global supply chain.

BenevolentAIs vision is to dramatically improve pharmaceutical R&D productivity across the board and to expand the drug discovery universe by making predictions in novel areas of biology. Currently, around half of late-stage clinical trials fail due to ineffective drug targets, resulting in only 15% of drugs advancing from mid-stage Phase 2 testing to approval.

Using a knowledge graph composed of chemical, biological and medical research and information, the companys AI machine learning models and algorithms can identify potential drug leads currently unknown in medical science and far faster than humans. While such systems will never replace scientists and clinicians, they can save both time and money. And the agnostic approach adopted by machine learning means such platforms can generate leads that may have been overlooked by traditional research.

The endeavour has already led to an in-house project on amyotrophic lateral sclerosis (ALS), ulcerative colitis, atopic dermatitis and programmes with partners on progressive kidney and lung diseases, as well as hard-to-treat cancers like glioblastoma.

The ability of machines to solve complex biological puzzles more rapidly than human experts has prompted increased investment in AI drug discovery by a growing number of large pharmaceutical companies.

And AI is also being harnessed in other areas of medicine, such as the analysis of medical images. This encompasses long-standing work on cancer scans and much more recent efforts to use computer power to identify COVID-19 from chest X-rays, including the open-access COVID-Net neural network.

The application of precision medicine to save and improve lives relies on good-quality, easily-accessible data on everything from our DNA to lifestyle and environmental factors. The opposite to a one-size-fits-all healthcare system, it has vast, untapped potential to transform the treatment and prediction of rare diseasesand disease in general.

But there is no global governance framework for such data and no common data portal. This is a problem that contributes to the premature deaths of hundreds of millions of rare-disease patients worldwide.

The World Economic Forums Breaking Barriers to Health Data Governance initiative is focused on creating, testing and growing a framework to support effective and responsible access across borders to sensitive health data for the treatment and diagnosis of rare diseases.

The data will be shared via a federated data system: a decentralized approach that allows different institutions to access each others data without that data ever leaving the organization it originated from. This is done via an application programming interface and strikes a balance between simply pooling data (posing security concerns) and limiting access completely.

The project is a collaboration between entities in the UK (Genomics England), Australia (Australian Genomics Health Alliance), Canada (Genomics4RD), and the US (Intermountain Healthcare).

Clearly, COVID-19 has been a wake-up call for the world. It seems this outbreak may be part of an increasingly frequent pattern of epidemics, fuelled by our hyper-connected modern world. As a result, medical experts are braced for more previously unknown Disease X threats in the years ahead as viruses jump from animals to humans and jet around the world.

Technology has helped create a world in which pathogens like COVID-19, SARS and Zika can spread. But technology, in the form of AI, can also provide us with the weapons to fight back.

Read this article:

AI can speed up the search for new treatments here's how - World Economic Forum

Americas solar farms have a bird problem. Utility companies have been finding bird carcasses littering the ground at their facilities for years, a strange and unexpected consequence of the national solar boom. No one was quite sure why this was happening, but it was clearly a problem for a type of energy that was billed as being environmentally friendly. So in 2013, a group of utilities, academics, and environmental organizations came together to form the Avian Solar Working Group to develop strategies to mitigate avian deaths at solar facilities around the US.

There was very little research about the impacts of solar on birds, says Misti Sporer, the lead environmental scientist at Duke Energy, an electric utility in North Carolina, and member of the working group. What does it mean when you find a dead bird? Nobody really knew. But simply getting the data on avian deaths at solar facilities proved challenging.

In 2016, a first-of-its-kind study estimated that the hundreds of utility-scale solar farms around the US may kill nearly 140,000 birds annually. Thats less than one-tenth of one percent of the estimated number of birds killed by fossil-fuel power plants (through collisions, electrocution, and poisoning), but the researchers expected that number to nearly triple as planned solar farms come online. The link between solar facilities and bird deaths is still unclear. One leading theory suggests birds mistake the glare from solar panels for the surface of a lake and swoop in for a landing, with deadly results. But that hypothesis is from a human perspective, says Sporer. Do birds even see the same way people do? We need to collect more data to form a complete picture.

Earlier this year, the Department of Energy awarded a team of researchers at Argonne National Laboratory in Illinois a $1.3 million contract to develop an artificial intelligence platform dedicated to studying avian behavior at large-scale solar facilities around the US. The researchers hope the data gathered by their system will help ornithologists unravel the mystery of why our feathered friends are dying in droves at solar farms. The important thing is to reduce solars environmental impact in every form, says Yuki Hamada, a biophysical scientist at Argonne who is leading the project. These avian issues are a concern and something that the renewable energy industry wants to understand and mitigate.

Only a few regions in the US have regulations that require solar operators to report avian deaths at their facilities; most of Americas large-scale solar farms dont bother with this time-consuming and morbid calculus. Those that do are limited in their ability to collect quality data and may only send surveyors to count bird carcasses at a solar farm once a month. While this helps solar plant operators understand how many birds are dying, it doesnt offer much insight into why theyre dying. For that, they need some real-time observations.

Read the original here:

Why Do Solar Farms Kill Birds? Call in the AI Bird Watcher - WIRED

CaptionBot, an AI program that applies captions to images, mistakenly described the members of the hip hop group the Wu-Tang Clan as a group of baseball players. This type of mistake often occurs because of the way certain demographics are represented in the data used to train an AI system. Credit: Walter Scheirer/CaptionBot.

In 2006, a trio of artificial intelligence (AI) researchers published a useful resource for their community, a massive dataset consisting of images representing over 50,000 different noun categories that had been automatically downloaded from the internet. The dataset, dubbed Tiny Images, was an early example of the big data strategy in AI research, whereby an algorithm is shown as many examples as possible of what it is trying to learn in order for it to better understand a given task, like recognizing objects in a photo. By uploading small 32-by-32 pixel images, the Tiny Images researchers were relying on the ability of computers to exhibit the same remarkable tolerance of the human visual system and recognize even degraded images. They also, however, may have unintentionally succeeded in recreating another human characteristic in AI systems: racial and gender bias.

A pre-print academic paper revealed that Tiny Images used several categories for images labeled with racial and misogynistic slurs. For instance, a derogatory term for sex workers was one category; a slur for women, another. There was also a category of images labeled with a racist term for Black people. Any AI system trained on the dataset might recreate the biased categories as it sorted and identified objects. Tiny Images was such a large dataset and its contents so small, that it would have been a herculean, perhaps impossible, task to perform quality control to remove the offensive category labels and images. The researchers, Antonio Torralba, Rob Fergus, and Bill Freeman, made waves in the artificial intelligence world when they announced earlier this summer that they would be pulling the whole thing from public use.

Biases, offensive and prejudicial images, and derogatory terminology alienates an important part of our community precisely those that we are making efforts to include, Torralba, Fergus, and Freeman wrote. It also contributes to harmful biases in AI systems trained on such data.

Developers used the Tiny Images data as raw material to train AI algorithms that computers use to solve visual recognition problems and recognize people, places, and things. Smartphone photo apps, for instance, use similar algorithms to automatically identify photos of skylines or beaches in your collection. The dataset was just one of many used in AI development.

While the pre-prints shocking findings about Tiny Images was troubling in its own right, the issues the paper highlighted are also indicative of a larger problem facing all AI researchers: the many ways in which bias can creep into the development cycle.

The big data era and bias in AI training data. The internet reached an inflection point in 2008 with the introduction of Apples iPhone3G. This was the first smartphone with viable processing power for general internet use, and, importantly, it included a digital camera that could be available to the user at a moments notice. Many manufacturers followed Apples lead with similar competing devices, bringing the entire world online for the first time. A software innovation that appeared with these smartphones was the ability of an app running on a phone to easily share photos from the camera to privately owned cloud storage.

This was the launch of the era of big data for AI development, and large technology companies had an additional motive beyond creating a good user experience: By concentrating a staggering amount of user-generated content within their own platforms, companies could exploit the vast repositories of data they collected to develop other products. The prevailing wisdom in AI product development has been that if enough data is collected, any problem involving intelligence can be solved. This idea has been extended to everything from face recognition to self-driving cars, and is the dominant strategy for attempting to replicate the competencies of the human brain in a computer.

But using big data for AI development has been problematic in practice.

The datasets used for AI product development now contain millions of images, and nobody knows what exactly is in them. They are too large to examine manually in an exhaustive manner. When it comes to the use of these sets, the data can be labeled or unlabeled. If the data is labeled (as was the case with Tiny Images), those labels can be tags that were taken from the original source, new labels assigned by volunteers or people who have been paid to provide them, or automatically generated by an algorithm trained to label data.

Dataset labels can be naturally bad, reflecting the biases and outright malice of the humans who annotated them, or artificially bad, if the mistakes are made by algorithms. A dataset could even be poisoned by malicious actors intending to create problems for any algorithms that make use of it. Additionally, some datasets contain unlabeled data, but these datasets, used in conjunction with algorithms that are designed to explore a problem on their own, arent the antidote for poorly labeled data. As is also the case for labeled datasets, the information contained within unlabeled datasets can be a mismatch with the real world, for instance when certain demographics are under- or over-represented in the data.

In 2016, Microsoft released a web app called CaptionBot that automatically added captions to images. The app was meant to be a successful demonstration of the companys computer vision API for third-party developers, but even under normal use, it could make some dubious mistakes. In one notable instance, it mislabeled a photo of the hip hop group Wu-Tang Clan as a group of baseball players. This type of mistake can occur when a bias exists in the dataset for a specific demographic. For example, Black athletes are often overrepresented in datasets assembled from public content on the internet. The prominent Labeled Faces in the Wild dataset for face recognition research contains this form of bias.

More problematic examples of this phenomenon have surfaced. Joy Buolamwini, a scholar at the MIT Media Lab, and Timnit Gebru, a researcher at Google, have shown that commonly used datasets for face recognition algorithm development are overwhelmingly composed of lighter skinned faces, causing face recognition algorithms to have noticeable disparities in matching accuracy between darker and lighter skin tones. Buolamwini, working with Deborah Raji, a student in her laboratory, has gone on to demonstrate this same problem in Amazons Rekognition face analysis platform. These studies have prompted IBM to announce it would exit the facial recognition and analysis market and Amazon and Microsoft to halt sales of facial recognition products to law enforcement agencies, which use the technology to identify suspects.

The manifestation of bias often begins with the choice of an application, and further crops up in the design and implementation of an algorithm well before any training data is provided to it. For instance, while its not possible to develop an algorithm to predict criminality based on a persons face, algorithms can seemingly produce accurate results on this task. This is because the datasets they are trained on and evaluated with have obvious biases, such as mugshot photos that contain easily identifiable technical artifacts specific to how booking photos are taken. If a development team believes the impossible to be possible, then the damage is already done before any tainted data makes its way into the system.

There are a lot of questions AI developers should consider before launching a project. Gebru and Emily Denton, also a researcher at Google, astutely point out in a recent tutorial they presented at the 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition that interdisciplinary collaboration is a good path forward for the AI development cycle. This includes engagement with marginalized groups in a manner that fosters social justice, and dialogue with experts in fields outside of computer science. Are there unintended consequences of the work being undertaken? Will the proposed application cause harm? Does a non-data driven perspective reveal that what is being attempted is impossible? These are some of the questions that need to be asked by development teams working on AI technologies.

So, was it a good thing for the research team responsible for Tiny Images to issue a complete retraction of the dataset? This is a difficult question. At the time of this writing, the published paper on the dataset has collected over 1,700 citations according to Google Scholar and the dataset it describes is likely still being used by those who have it within their possession. The retraction of Tiny Images creates a secondary problem for researchers still working with algorithms developed with Tiny Images, many of which are not flawed and worthy of further study. The ability of researchers to replicate a study is a scientific necessity, and by removing the necessary data, this becomes impossible. And an outright ban on technologies like face recognition may not be a good idea. While AI algorithms can be anti-social in contextslikepredictive policing, they can be socially acceptable in others, such as human-computer interaction.

Perhaps one way to address this is to issue a revision of a dataset that removes any problematic information, notes what has been removed from the previous version, and provides an explanation for why the revision was necessary. This may not completely remove the bias within a dataset, especially if it is very large, but it is a way to address specific instances of bias as they are discovered.

Bias in AI is not an easy problem to eliminate, and developers need to react to it in a constructive way. With effective mitigation strategies, there is certainly still a role for datasets in this type of work.

See the original post:

How to make AI less racist - Bulletin of the Atomic Scientists

An AI-controlled fighter jet will battle a US Air Force pilot in a simulated dogfight next week and you can watch the actiononline.

The clashis the culmination of DARPAsAlphaDogfight competition, which the Pentagons mad science wing launched to increase trust in AI-assisted combat.DARPA hopes this will raise support for using algorithms in simpler aerial operations,so pilots can focus on more challenging tasks, such as organizing teams of unmanned aircraft across the battlespace.

The three-day event was scheduled to take place in-person in Las Vegas from August 18-20, but the COVID-19 pandemic led DARPA to move the event online.

Before the teams take on the Air Force on August 20, the eight finalists will test their algorithms against five enemy AIs developed byJohns Hopkins Applied Physics Laboratory. Theirmission is to recognize and exploit the weaknesses and mistakes of their rivals, and maneuver to a position of control beyond the enemys weapon employment zone.

[Read:Gamers will teach AI how to control military drone swarms]

The next day, the teams will compete against each other in a round-robin tournament. The top four will then enter a single-elimination tournament for theAlphaDogfight Trials Championship. Finally, the winner will take on a US Air Force fighter pilot flying a virtual reality F16 simulator, to test whether their system can vanquish the militarys elite.

The contest aims to develop a base of AI developers for DARPAs Air Combat Evolution (ACE) program, which is trying to further automate aerial combat. Dogfighting is expected to be a rare part of this in the future, but the duels will provide evidence that AI can handle a high-end fight.

Regardless of whether the human or machine wins the final dogfight, the AlphaDogfight Trials is all about increasing trust in AI, said Colonel Dan Animal Javorsek, program manager in DARPAs Strategic Technology Office. If the champion AI earns the respect of an F-16 pilot, well have come one step closer to achieving effective human-machine teaming in air combat, which is the goal of the ACE program.

You can watch the battles unfold by signing up online. Registration closes on August 17 for US citizens, and on August 11 for everyone else. If youre not a US citizen, youll also need to submit one of DARPAs visit request forms.

Published August 10, 2020 12:28 UTC

Original post:

A US Air Force pilot is taking on AI in a virtual dogfight here's how to watch it - The Next Web

AI isnt new to HR and recruitment. Source: Shutterstock

In an uncertain economy and rocky jobs market, any technology that takes the burden off the onboarding process is a welcome tonic for an overworked HR department especially when, in many cases, those new recruits will be joining the team remotely.

While, like in many other sectors, artificial intelligence (AI) is beginning to prove a boon to Human Resources, some applications of the intelligent, process-expediting technology arent yet watertight. A recent Sage report titled The changing face of HR consulted organizations on their propensity to adopt the latest tech for HR functions. 43% of respondents believed their firms will not keep up with tech changes over the coming decade. This creates a somewhat troubling outlook.

For starters, overseeing a traditionally (and inherently) human set of functions, HR teams are perhaps prone to lag when it comes to adopting the latest technology. But discussions around AI in HR also conjure images of recruitment bias, where algorithms under the hood of predictive hiring tools have demonstrated bias against African-American-sounding names and female applicants.

According to that report, 24% of quizzed companies are already using AI for talent acquisition (in the form of automation), while 56% claim they will adopt such tech in the coming year.

But there are signs of increasing uptake of technologies in HR, and applications of AI go a lot further in the recruitment and onboarding process than just filtering through thousands of applications.

The stages from interview to offer to negotiation to acceptance and the ease with which you fly through these tells you a whole lot about the business youve just agreed to join. In fact, onboarding is (to many) something of a magic moment, in which new employees decide to stay engaged or become disengaged.

AI steps up to simplify tasks (negating the need for manual document back-and-forths), automating otherwise arduous account setups, and providing feedback on the whole affair to make the next hire smoother. It can track tasks, prompt responses, and even answer questions that may arise from new hires. Here are a few use cases in more detail:

Document generation: Using natural language processing (NLP), organizations can auto-generate offer letters, contracts, and other vital documents with employees. A human still needs to validate the output and ensure that it is signed properly, though.

FAQ chatbots: Most new recruits will have a lot of basic questions (regarding connecting to the office WiFi, setting up an email account, or log-off/screen-lock protocols, among others). A chatbot is a strong way of addressing FAQs whilst retaining a sense of back-and-forth. It can also be continuously tweaked and upgraded as new queries arise.

Networking: Building relationships with peers and team members is crucial for new hires to integrate into an organization, increase productivity, and become engaged employees. Using organizational network analysis (ONA), organizations can understand which relationships new employees must cultivate to be productive, and introduce new hires to critical points of contact in their team and in the organization.

Feedback analysis: Like with literally any process on planet earth (and, Id envisage, beyond), feedback and insight are key to continuous improvement. In order to know how to fine-tune the path through recruitment, AI can provide HR professionals with the tools to understand direct and indirect feedback. Using NLP again, HR managers can extract quality insights from large quantities of textual feedback. This can allow HR managers to gauge themes, employee sentiment, and the overarching effectiveness of HR processes.

Where AI really comes into its own is in its ability to (quickly) adjust what information is required, presented, and completed, based on the specific job in question. For any firm with a sizeable employee base, these nuances can give rise to lag times, inaccuracies, and otherwise poor practice. An intelligent system can give proper permissions, schedule meetings required to understand a role, and even develop tools to help that understanding.

With AI, onboarding doesnt have to happen within regular business hours or at a fixed office location. AI and chatbots can work around the clock, guiding a new hire through all aspects of onboarding and answering questions as they arise. With HR teams busier than ever in coordinating and responding to remote working issues, the onboarding process is one space where tech can really come in handy, allowing new hires to integrate more quickly [] even before their first day on the job, says Susan Power, founder and CEO of Power HR.

Gamification can be one way to utilize AI and set apart your onboarding procedure in one fell, albeit intricate swoop. Adding competitive, enjoyable, game-like elements to the onboarding process can make it easier for human minds to absorb and retain information. AI can help this customized experience both in the recruitment process (with cognitive ability/competency tests) and afterward.

Covid-19 has disrupted many companies typical routine when it comes to onboarding employees. Cognitive automation tools can simplify the process for new hires that may start remotely, and lessen the alienation that can come from limited face-to-face training time.

The crux of the matter is not actually a crux at all, but rather an ongoing navigation of the intersection between human involvement and AI efficiencies. This will indeed change from company to company. The fact is that you cant take the human out of Human Resources. People will remain integral to a robust, personable HR strategy. You can, however, add AI technology at intelligent onboarding touchpoints, diverting human time away from inane clerical tasks to forming real bonds with candidates and growing teams in the most positive of ways.

Read the rest here:

How AI is taking the pain out onboarding for the HR team - Tech Wire Asia

A new study will apply Artificial Intelligence (AI) technology to look for predictive patterns in the vital signs of COVID-19 patients that could alert the medical team about any deterioration.

The Manchester-based trial is sponsored by The Christie NHS Foundation Trust together with the Manchester University NHS Foundation Trust (MFT) with additional participation from Aptus Clinical and core AI capabilities provided by Zenzium, Ltd. (Cheshire, UK).

The COSMIC-19 (COntinious Signs Monitoring In Covid-19 patients) pilot study aims to recruit 60 inpatients on general wards who are suspected or confirmed to have COVID-19. Approximately 10-20% of hospital inpatients with COVID-19 will need intensive care. The patients on the trial will be monitored for 20 days until either placed on a ventilator or discharged from hospital.

The study will use wireless wearable sensors to automatically collect each patients vital signs together with clinical data and observations. Zenzium will then apply its AI technology to look for predictive patterns in the patients vital signs that could alert the medical team if the patient is deteriorating. If the prediction indicates that the patient needs critical care, the medical team can intervene earlier to give patients the best chance of recovery. Zenziums core technology, including DeepHRV, is based on Deep Learning as applied to time-series measurements and data.

We are extremely excited to apply our AI technology based on time-series Deep Learning including DeepHRV to this challenge with the potential to make a substantial impact on patient outcomes, said Anthony D. Bashall, Managing Director & Founder of Zenzium.

Unfortunately some patients who are suffering from COVID-19 on our hospital wards can become seriously unwell. By using this system, we hope to be able to identify these patients early and this may mean we can optimize their management without the need for them to go to intensive care, said Professor Fiona Thistlethwaite, medical oncologist at The Christie, who will lead the trial.

Related Links:

Zenzium, Ltd.

Link:

AI Technology Detects Deterioration in COVID-19 Patients by Identifying Predictive Patterns in Their Vital Signs - HospiMedica

News Release

Wednesday, August 5, 2020

Collaborative network to enlist medical imaging and clinical data sciences to reveal unique features of COVID-19.

The National Institutes of Health has launched the Medical Imaging and Data Resource Center (MIDRC), an ambitious effort that will harness the power of artificial intelligence and medical imaging to fight COVID-19. The multi-institutional collaboration, led by the National Institute of Biomedical Imaging and Bioengineering (NIBIB), part of NIH, will create new tools that physicians can use for early detection and personalized therapies for COVID-19 patients.

This program is particularly exciting because it will give us new ways to rapidly turn scientific findings into practical imaging tools that benefit COVID-19 patients, said Bruce J. Tromberg, Ph.D., NIBIB Director. It unites leaders in medical imaging and artificial intelligence from academia, professional societies, industry, and government to take on this important challenge.

The features of infected lungs and hearts seen on medical images can help assess disease severity, predict response to treatment, and improve patient outcomes. However, a major challenge is to rapidly and accurately identify these signatures and evaluate this information in combination with many other clinical symptoms and tests. The MIDRC goals are to lead the development and implementation of new diagnostics, including machine learning algorithms, that will allow rapid and accurate assessment of disease status and help physicians optimize patient treatment.

This effort will gather a large repository of COVID-19 chest images, explained Guoying Liu, Ph.D., the NIBIB scientific program lead on this effort, allowing researchers to evaluate both lung and cardiac tissue data, ask critical research questions, and develop predictive COVID-19 imaging signatures that can be delivered to healthcare providers.

Maryellen L. Giger, PhD, the A.N. Pritzker Professor of Radiology, Committee on Medical Physics at the University of Chicago, is leading the effort, which includes co-Investigators Etta Pisano, MD, and Michael Tilkin, MS, from the American College of Radiology (ACR), Curtis Langlotz, MD, PhD, and Adam Flanders, MD, representing the Radiological Society of North America (RSNA), and Paul Kinahan, PhD, from the American Association of Physicists in Medicine (AAPM).

This major initiative responds to the international imaging communitys expressed unmet need for a secure technological network to enable the development and ethical application of artificial intelligence to make the best medical decisions for COVID-19 patients, added Krishna Kandarpa, M.D., Ph.D., director of research sciences and strategic directions at NIBIB. Eventually, the approaches developed could benefit other conditions as well.

The MIDRC will facilitate rapid and flexible collection, analysis, and dissemination of imaging and associated clinical data. Collaboration among the ACR, RSNA, and AAPM is based on each organizations unique and complementary expertise within the medical imaging community, and each organizations dedication to imaging data quality, security, access, and sustainability.

About the National Institute of Biomedical Imaging and Bioengineering (NIBIB):NIBIBs mission is to improve health by leading the development and accelerating the application of biomedical technologies. The Institute is committed to integrating engineering and physical science with biology and medicine to advance our understanding of disease and its prevention, detection, diagnosis, and treatment. NIBIB supports emerging technology research and development within its internal laboratories and through grants, collaborations, and training. More information is available at the NIBIB websitehttps://www.nibib.nih.gov.

About the National Institutes of Health (NIH):NIH, the nation's medical research agency, includes 27 Institutes and Centers and is a component of the U.S. Department of Health and Human Services. NIH is the primary federal agency conducting and supporting basic, clinical, and translational medical research, and is investigating the causes, treatments, and cures for both common and rare diseases. For more information about NIH and its programs, visit http://www.nih.gov.

NIHTurning Discovery Into Health

###

Excerpt from:

NIH harnesses AI for COVID-19 diagnosis, treatment, and monitoring - National Institutes of Health

The UK Government has said that businesses must update their risk assessments to factor in the dangers associated with coronavirus. This means that, in order to remain compliant and avoid any future liability issues, businesses need to take action to mitigate the impact of the virus on their workforce.

The government has clearly warned that any food business which fails to complete a risk assessment that takes COVID-19 into account could be in a breach of health and safety law. Employers therefore need to prioritise managing the risks properly. They need to consider the wider context of the workforce to ensure there are no weaknesses in procedures that may put them and their employees at risk, Will Cooper, CEO and founder at Delfin Health, explained.

Digital health tech companies Delfin Health and DocHQ have created a new tool that leverages artificial intelligence to predict, monitor and test the health and safety of the diverse workforce that operates in the food sector.

The very nature of food production means there are many different functions and roles within food manufacturing, Cooper observed. And, because these workers are directly involved in food processing and the handling of food production, employers are required by law to follow specific government guidelines.

Dubbed Klarity, the AI can give food businesses a real-time clinical understanding of the health of their workers across various job functions, from food inspectors, food handlers, packers, managers and cleaners, to maintenance contractors and delivery workers.

Tools like Klarity can both mitigate any potential employer liability risks and provide a long-term solution to a health crisis.

This could become particularly important for essential food businesses if there is a second spike in COVID-19 that results in further lockdowns, either locally or nationally.

It can help manufacturers stay operational during a potential second lockdown. Due to food being an essential industry, we have already seen them continue to operate during the first wave of COVID-19, albeit in a limited way which is putting their key workers at risk. If these are to remain open, they need to be able to monitor the health and safety of their staff in the most efficient way.

While food businesses have remained largely operational during the various national lockdowns, certain facilities have had to be shuttered due to localised outbreaks. Cases in the meat sector - from Germany and the US to the UK and the Netherlands - have highlighted issues that Cooper believes everyone operating in the food industry would do well to take heed of.

Its not enough for employers to simply rely on people using the test and trace government solution which tests only symptomatic people. There are cases of the virus spreading rapidly throughout food manufacturing units in the US and Germany and no doubt elsewhere due to the conditions of the facilities which typically involve close contact. Its also highly likely these units relied on just testing or returning home symptomatic people. It requires a systematic process of regular testing.

Cooper does not believe it is possible to simply test the entire workforce due to cost and capacity restraints. Digital AI platform Klarity takes a different approach.

One of the important roles in COVID-19 transmission in this pandemic, especially at this stage, is being played by asymptomatic individuals. Although theoretically speaking the easiest solution might be to apply systematic testing to general population, doing that would become technically unfeasible due to lack of resources and sky rocketing expenditures.

We have developed a tailored solution that guarantees a consistent testing methodology developed to filter infected asymptomatic employees among large taskforce pools. Thus, our solution can meet the requirements of different sectors, reducing the number of tests, decreasing uncertainty in the workplace and potentially mitigating future outbreaks.

How does Klarity work?

Cooper elaborated: The explainable AI that we have developed asks a series of questions about a persons health history, family health history and current lifestyle. The algorithm within Klarity has been developed using data from one of the largest patient datasets in the world containing over 6.5 million patient years of both medical and lifestyle data.

We use this information to predict the mortality risk of a patient if they contract COVID-19. Our technology further combines optional daily symptom checking and live virus and antibody testing methodologies to track asymptomatic patients before they transfer this disease to people around them, he told FoodNavigator.

The various testing methodologies, which include group and randomised testing, allow employers to reduce the amount of testing required and minimise the risk of an outbreak in the currently active workforce, in particular by identifying asymptomatic cases.

The testing process is guided by healthcare professionals who also interpret the results based on World Health Organisation protocols and third-party validation including Polymerase Chain Reaction (PCR), Enzyme-Linked Immunosorbent Assay (ELISA) and, where relevant, rapid antibody tests.

Our solution allows the reduction of testing yet, through group and randomised testing, identifies the virus quickly.

Cooper believes that, as well as reducing the risk of outbreaks in a food business, the use of Klarity would also serve to reassure employees that they are safe at work.

Not everyone will feel comfortable sharing details of their medical history and lifestyle choices, particularly with a programme provided through their employer. For this reason, Cooper stressed, data protection is key.

Data privacy is one of our utmostpriorities as we are processing sensitive and private patient information. Patient have complete control of their data, they can share and revoke consent. Moreover, no data ever leaves the platform. We dont share sensitive personal health data but only aspects necessary to help employers keep employees safe. Our platform keeps up to date with the ever-changing policies and regulations so that the companies dont have to worry about GDPR rules and employee rights, he told us.

In terms of encryption, we use a highly secure (Quantum resistant), distributed and highly configurable storage mechanism; allowing citizens the ability to source, store and share (by record or down to individual fields) their data.

More here:

Leveraging AI to reduce COVID-19 risk: 'It's not enough to rely on test and trace' - FoodNavigator.com